2004 – 2011: Part-time Associate Professor at Hedmark University College in Nature- and knowledge tourism

1995 – 2000: Part-time assistant in Physical Geography at University of Oslo

Cooperation

Strategic long-term co-operation with Geosciences (Oslo) on vegetation-atmosphere interactions, and with the Norwegian Biodiversity Information Centre on Nature in Norway (NiN).

Regular co-operation with colleagues at Geosciences (Bergen), Norwegian Institute for Bioeconomy Research, Norwegian Centre for Rural Research, Inland Norway University of Applied Sciences, The Norwegian Institute for Water Research and The Norwegian Institute for Nature Research.

Collaborating with fellows at Stockholm University (Physical Geography and Quaternary Geology), University of Copenhagen (Forest & Landscape), University of Turku (Geography and Geology), and Icelandic Institute of Natural History (Vegetation Mapping).

Abstract Questions Vegetation mapping based on field surveys is time-consuming and expensive. Distribution modelling might be used to overcome these challenges. What is the performance of distribution modelling of vegetation compared to traditional vegetation mapping when projected locally? Does the modelling performance vary among ecosystems? Does vegetation type distribution and abundance influence the modelling performance? Location Gravfjellet, Øystre Slidre commune, southern Norway. Methods Two comparable neighbouring areas, each of 4 km2, were mapped for species-defined vegetation types. One area was used for model training, the other for model projection. Maximum entropy models were run for six vegetation types, two from each of the ecosystems present in the area: forest, wetland and mountain heath- and shrublands. For each ecosystem, one locally abundant and one locally rare vegetation type were tested. AUC, the area under the receiver operating curve, was used as the model selection criterion. Environmental variables (n = 9) were selected through a backwards selection scheme, and model complexity was kept low. The models were evaluated using independent data. Results Distribution modelling of vegetation types by local projection gave high AUC values, and the results were supported by the evaluation using independent data. The modelling ability was not affected by ecosystem differences. A negative relationship between the number of points used to train the models and the AUC value before evaluation suggests that models for locally rare vegetation types had better predictive performance than the models for abundant types. This result was not significant after evaluation. Conclusion Provided that relevant explanatory variables are available at an appropriate scale, and that field-validated training points are available, distribution modelling can be used for local projection of the six tested vegetation types from the boreal–alpine ecotone.

Expanding high elevation and high latitude forest has contrasting climate feedbacks through carbon sequestration (cooling) and reduced surface reflectance (warming), which are yet poorly quantified. Here, we present an empirically-based projection of mountain birch forest expansion in south-central Norway under climate change and absence of land use. Climate effects of carbon sequestration and albedo change are compared using four emission metrics. Forest expansion was modeled for a projected 2.6 °C increase of summer temperature in 2100, with associated reduced snow cover. We find that the current (year 2000) forest line of the region is circa 100 m lower than its climatic potential due to land use history. In the future scenarios, forest cover increased from 12 to 27% between 2000 and 2100, resulting in a 59% increase in biomass carbon storage and an albedo change from 0.46 to 0.30. Forest expansion in 2100 was behind its climatic potential, forest migration rates being the primary limiting factor. In 2100, the warming caused by lower albedo from expanding forest was 10 to 17 times stronger than the cooling effect from carbon sequestration for all emission metrics considered. Reduced snow cover further exacerbated the net warming feedback. The warming effect is considerably stronger than previously reported for boreal forest cover, because of the typically low biomass density in mountain forests and the large changes in albedo of snow-covered tundra areas. The positive climate feedback of high latitude and high elevation expanding mountain forests with seasonal snow cover exceeds those of afforestation at lower elevation, and calls for further attention of both modelers and empiricists. The inclusion and upscaling of these climate feedbacks from mountain forests into global models is warranted to assess the potential global impacts.

During recent decades, forests have expanded into new areas throughout the whole of Norway. The processes explained as causing the forest expansion have focused mainly on climate or land use changes. To enable a spatially explicit separation of the effects following these two main drivers behind forest expansion, the authors set out to model the potential for natural forest regeneration following land use abandonment, given the present climatic conditions. The present forest distribution, a number of high-resolution land cover maps, and GIS methods were used to model the potential for natural forest regeneration. Furthermore, the results were tested with independent local models, explanatory variables and predictive modelling. The modelling results show that land use abandonment, in a long-term perspective, has the climatic and edaphic potential to cause natural forest regeneration of 48,800 km2, or 15.9% of mainland Norway. The future natural forest regeneration following land use change or abandonment can now be spatially separated from the effects of climate changes. The different independent model tests support the main findings, but small fractions of the modelled potential natural forest regeneration will probably be caused by other processes than land use abandonment.

Long-term and varied land use has had a major influence on the vegetation in rural Norway, and the traditional open landscapes are now being replaced by forests. In the present investigation, we assess and quantify structural vegetation changes caused by changes in land use and climate. Up-to-date actual vegetation maps from three rural study areas were compared with interpreted historical vegetation maps and potential natural vegetation (PNV) models. Our findings indicate that the present vegetation structure is strongly influenced by land use. In the studied sites, 56–66% of the areas presently have another vegetation type than expected from a natural state (PNV). The mean turnover of vegetation types in the study areas during the past 35–40 years was 25%. Our study highlights that the influence of land-use needs to be accounted for when considering the effects of climate change.

The purpose of the study was to explore and compare three different methods for modelling potential natural vegetation (PNV), a hypothetic natural state of vegetation that shows nature's biotic potential in the absence of human influence and disturbance. The vegetation was mapped in a south-central Norwegian mountain region, in a 34.2 km2 area around the village of Beitostølen, in 2009. The actual vegetation map (AVM) formed the basis for the development of PNV using three different modelling methods: (1) an expert-based manual modelling (EMM), (2) rule-based envelope GIS-modelling (RBM), and (3) a statistical predictive GIS-modelling method (Maxent). The article shows that the three modelling methods have different advantages, challenges and preconditions. The findings indicate that: (1) the EMM method should preferably be used only as a supplementary method in highly disturbed areas, (2) both the RBM and the Maxent methods perform well, (3) RBM performs especially well, but also Maxent are more objective methods than EMM and they are much easier to develop and re-run after model validation, (4) Maxent probably underestimates the potential distribution of some vegetation types, whereas RBM overestimates, (5) the Maxent output is relative probabilities of distribution, giving higher model variation than RBM.

Pilgrims travel along the main reopened St Olav pilgrim routes in Norway and visit a variety of cultural heritage types. These routes are part of a value creation programme, in which the management authorities try to increase the numbers of pilgrims. At the same time, forest regrowth is reported to reduce the landscape experience of pilgrims and to biophysically change the cultural heritage sites. However, no studies have been reported on the spatial encroachments of forests along the pilgrim routes. The purpose of this study is to analyse where forest regrowth along the main reopened pilgrim routes in Norway will appear, given the present climatic conditions, and to assess the spatial overlap of future forest regrowth with cultural heritage sites. A potential forest model and a cultural heritage sites database were combined with several baseline geographical data layers and spatially joined in geographical information systems. The results show that most of the future forest regrowth will appear in mountainous parts of the pilgrim routes, whereas many hunting sites, tradition sites and other cultural heritage sites will be overgrown by young forests. Therefore, management efforts to keep the main pilgrim routes open need to be strengthened and directed towards future risks.

The Norwegian landscape is changing as a result of forest regeneration within the cultural landscape, and forest expansion has impacts on accessibility, visibility, and landscape aesthetics, thereby affecting the country's tourism industry. This study aimed at identifying the potential areas of forest regeneration and anticipated subsequent landscape effects on different categories of tourist locations in southern Norway. Deforested areas with a potential for forest regeneration were identified from several map sources by GIS-analyses, and 180 tourist locations were randomly selected from the Norwegian national tourism database (Reiselivsbasen), and then buffered by 2 km radius for land cover classes. The findings revealed that approximately 15% of southern Norway has the climatic potential for future forest regeneration, in addition to 5% of cultivated land. Future forest regeneration will affect the landscapes surrounding the tourist locations of rural south Norway, and while the potential is nationwide, it is not uniformly distributed. Two important tourist landscape regions seem especially exposed to forest regeneration: the coastal heath region and the mountain landscapes. Large parts of these areas do not have sufficient numbers of domestic grazing animals necessary to maintain the present character of the landscape.

The coastal heath region along the western coast of Norway, dominated by Calluna vulgaris, is undergoing rapid change. Vegetation changes are caused by changes in management, including reduced frequency or abandonment of periodic heath burning and reduced cutting and grazing. The islands of Froan, in the outermost part of Sør-Trøndelag County in mid-western Norway, are dominated by coastal heath in a state of recession due to reduced traditional land use. The coastal heath is acknowledged as vulnerable and valuable by national environmental authorities, and local landscape management is supported by different national subsidies. The authors mapped the vegetation on Froan and used rule-based GIS-modelling to predict the relative potential for future vegetation changes. The model was based on a range of map layers, including management themes such as history of heath burning and peat removal, current practices of sheep grazing, and also themes derived from the vegetation map, such as soil nutrients, soil moisture and present management status. The resulting model output provides relative probabilities of future changes under different land-use scenarios, and highlights where management efforts should be focused in order to maintain the traditional landscape character.

Extensive landscape and vegetation changes are apparent within southern Norway, specifically the expansion of forests into new areas and to higher altitudes. Two main processes are believed to cause these changes: regrowth after abandoned human utilisation and recent climate changes. The purpose of this article is to elucidate ways of separating the effects of these two processes on spatiotemporal changes in the upper forest limits using examples from southern Norway. Examples from two spatial scales are implemented, a vegetation map study of a mountain region in south-east Norway and a national map-based study of south Norway. The findings show that multiple methods are necessary to understand the forest limit changes and that the research focus should be on the separation of potential drivers, specifically climate improvements and land-use changes.

For almost 40 years the Norwegian Forest and Landscape Institute (Norsk institutt for skog og landskap) has mapped vegetation in Norway. In total, just over 10 % of the country’s land area has been mapped, most of which is in the mountain regions. The resultant vegetation maps are the closest Norway has to an ecological map series. Many secondary map themes can be derived from the vegetation map and the digital format allows a wealth of both spatial and temporal GIS-analyses. Accordingly, there are many user groups and topics of interest. During 2009 the aim is to make the institute’s vegetation maps available to all via the Internet in a seamless database.